Publications in Scientific Journals:

A. Papp, C. Wiesmeyr, M. Litzenberger, H. Garn, W. Kropatsch:
"Train Detection and Tracking in Optical Time Domain Reflectometry (OTDR) Signals";
Pattern Recognition, 9796 German Conference on Pattern Recognition (2016), Lecture Notes in Computer Science (LNCS); 320 - 331.

English abstract:
We propose a novel method for the detection of vibrations
caused by trains in an optical fiber buried nearby the railway track.
Using optical time-domain reflectometry vibrations in the ground caused
by different sources can be detected with high accuracy in time and
space. While several algorithms have been proposed in the literature for
train tracking using OTDR signals they have not been tested on longer
recordings. The presented method learns the characteristic pattern in the
Fourier domain using a support vector machine (SVM) and it becomes
more robust to any kind of noise and artifacts in the signal. The point-
based causal train tracking has two stages to minimize the influence of
false classifications of the vibration detection. Our technical contribution
is the evaluation of the presented algorithm based on two hour long
recording and demonstration of open problems for commercial usage.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.